abbey-knowledge-freshness-and-session-review-guidance.md

July 17, 2026

Implemented repository-aware knowledge freshness, AI auto-refresh integration, and improved session review guidance.

Tags: Abbey Root

abbey-knowledge-freshness-and-session-review-guidance.md

Summary

This session focused on making Abbey AI context repository-aware instead of simply checking whether a knowledge snapshot existed. Abbey Knowledge now records repository metadata and determines whether the generated snapshot is still valid based on the actual project state. At the same time, the AI session review prompt was refined to produce more decisive, evidence-driven recommendations, with regression tests added for both features.

Accomplishments

• Added repository-aware freshness metadata to Abbey Knowledge.
• Implemented repository manifest hashing based on selected project content.
• Added abbey knowledge status to report FRESH, STALE, MISSING, and INVALID knowledge states.
• Added abbey knowledge ensure with optional automatic rebuilding.
• Integrated knowledge freshness checks into abbey ai and Abbey context generation.
• Added configurable AI auto-build behavior through ABBEY_AI_AUTO_BUILD_KNOWLEDGE.
• Refactored repository manifest generation into the shared scripts/abbey_knowledge_manifest.py helper to eliminate duplicated logic.
• Improved abbey session review guidance to require evidence-based recommendations and clearer separation between validation and suggested work.
• Expanded regression coverage:
  • 22 Abbey AI regression tests
  • 9 Abbey Knowledge regression tests
  • 8 Session Review prompt tests
• Verified all syntax checks, Python compilation, and regression tests completed successfully.

Lessons Learned

Generated AI context should be treated like any other derived artifact—it requires validation against the current repository instead of assuming it remains correct over time.

Moving repository manifest generation into a shared helper reduced duplication and established a single source of truth for determining knowledge freshness. The regression tests also demonstrated the value of testing observable behavior rather than implementation details, allowing future refactoring without weakening confidence in the feature.

Next Steps

• Commit the repository-aware knowledge freshness implementation.
• Continue expanding Abbey Knowledge with additional reusable AI context sources.
• Apply the same repository freshness principles to future generated artifacts where appropriate.